---
abstractspacing: double
fontsize: 12pt
margin: 2cm
urlcolor: darkblue
linkcolor: Mahogany
citecolor: Mahogany
spacing: single
bibliography: references.bib
biblio-style: apalike
output:
  pdf_document:
    citation_package: natbib
    fig_caption: no
    number_sections: no
    keep_tex: no
    toc: no
    toc_depth: 3
    template: article-template.latex
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE,cache=TRUE)
knitr::opts_chunk$set(fig.width=7, fig.height=6, out.width = '70%', fig.align = "center") 
rm(list=ls())
library(remotes)
library(kableExtra)
library(haven)
library(tidyverse)
library(ivmodel)
library(doParallel)
library(foreach)
library(estimatr)
require(AER)
library(lfe)
library(glue)
#path <- "/Users/ziwenzu/Dropbox/research/IV/IV Sensitivity/LLXZ_rep"
path <- "~/Dropbox/ProjectZ/IV Sensitivity/LLXZ_rep"
setwd(path)
knitr::opts_knit$set(root.dir = path)
#install_github("apoorvalal/ivDiag")
library(ivDiag)
# number of cores
cores <- 15
```

## @chong2019

| Replication Summary | |
|---------|---------------------|
| Unit of analysis | household |
| Treatment | actual proportion of households treated in the locality |
| Instrument | treatment assignment in get-out-to-vote campaigns |
| Outcome | voted in 2013 presidential election |
| Model | Table4(1)|

```{r ajps_Chong_etal_2019}
df <-readRDS("./rawdata/ajps_Chong_etal_2019.rds")
D <-"ratio_treat"
Y <- "elecc_presid2013"
Z <- c("D2D30", "D2D40", "D2D50")
controls <-c("age", "married", "children", "num_children",
             "employed", "languag", "yrseduc", "bornloc",
             "hh_asset_index", "log_pop", "mujeres_perc",
             "pob_0_14_perc", "pob_15_64_perc", "pob_65mas_perc",
             "analfabetos_perc", "asiste_escuela_perc", 
             "TASA_women", "TASA_men", "electricidad_perc",
             "agua_perc", "desague_perc", "basura_perc",
             "fono_fijo_perc", "fono_cel_perc", "ocupantes", 
             "Rural",  "distancia2_final", "db_age", 
             "db_married", "db_children", "db_num_children", 
             "db_employed", "db_languag", "db_yrseduc", 
             "db_bornloc", "db_hh_asset_index", "db_log_pop", 
             "db_mujeres_perc", "db_pob_0_14_perc", 
             "db_pob_15_64_perc", "db_pob_65mas_perc", 
             "db_analfabetos_perc", "db_asiste_escuela_perc",
             "db_TASA_women", "db_TASA_men", "db_electricidad_perc",
             "db_agua_perc", "db_desague_perc", "db_basura_perc",
             "db_fono_fijo_perc", "db_fono_cel_perc", 
             "db_ocupantes", "db_Rural", "db_distancia2_final",
             "dpto1", "elecc_presid2008", "db_elecc_presid2008")
cl <- "loc"
FE <- NULL
weights<-NULL
(g<-ivDiag(data=df, Y=Y, D=D, Z=Z, controls=controls, FE =FE, 
  cl =cl,weights=weights, cores = cores))
```

```{r, cache = FALSE}
plot_coef(g)
```


```{r ajps_Chong_etal_2019_sav, echo = FALSE}
save(g, file="./estimate/Chong2019.RData")
```

